Intensive care unit caseload and workload and their association with outcomes in critically unwell patients: a large registry-based cohort analysis

Background Too high or too low patient volumes and work amounts may overwhelm health care professionals and obstruct processes or lead to inadequate personnel routine and process flow. We sought to evaluate, whether an association between current caseload, current workload, and outcomes exists in intensive care units (ICU). Methods Retrospective cohort analysis of data from an Austrian ICU registry. Data on patients aged ≥ 18 years admitted to 144 Austrian ICUs between 2013 and 2022 were included. A Cox proportional hazards model with ICU mortality as the outcome of interest adjusted with patients’ respective SAPS 3, current ICU caseload (measured by ICU occupancy rates), and current ICU workload (measured by median TISS-28 per ICU) as time-dependent covariables was constructed. Subgroup analyses were performed for types of ICUs, hospital care level, and pre-COVID or intra-COVID period. Results 415 584 patient admissions to 144 ICUs were analysed. Compared to ICU caseloads of 76 to 100%, there was no significant relationship between overuse of ICU capacity and risk of death [HR (95% CI) 1.06 (0.99–1.15), p = 0.110 for > 100%], but for lower utilisation [1.09 (1.02–1.16), p = 0.008 for ≤ 50% and 1.10 (1.05–1.15), p < 0.0001 for 51–75%]. Exceptions were significant associations for caseloads > 100% between 2020 and 2022 [1.18 (1.06–1.30), p = 0.001], i.e., the intra-COVID period. Compared to the reference category of median TISS-28 21–30, lower [0.88 (0.78–0.99), p = 0.049 for ≤ 20], but not higher workloads were significantly associated with risk of death. High workload may be associated with higher mortality in local hospitals [1.09 (1.01–1.19), p = 0.035 for 31–40, 1.28 (1.02–1.60), p = 0.033 for > 40]. Conclusions In a system with comparably high intensive care resources and mandatory staffing levels, patients’ survival chances are generally not affected by high intensive care unit caseload and workload. However, extraordinary circumstances, such as the COVID-19 pandemic, may lead to higher risk of death, if planned capacities are exceeded. High workload in ICUs in smaller hospitals with lower staffing levels may be associated with increased risk of death. Supplementary Information The online version contains supplementary material available at 10.1186/s13054-024-05090-z.


Table S 1
Cox proportional hazards regression with ICU mortality as endpoint.The main variable of interest, an interaction between workload based on the TISS-28-Score and caseload based on bed occupancy was modelled as a time-dependent covariable.SAPS3, sex, year of discharge, type of ICU, time of day, and calendar day were also included in the model and ICUID serves as a cluster variable

0001 Non-working day vs. working day
Results of a Cox proportional hazards regression with ICU mortality as endpoint.The main variable of interest, an interaction between workload based on the TISS-28-Score and caseload based on bed occupancy was modelled as a time-dependent covariable and is depicted here.SAPS3, sex, year of discharge, type of ICU, time of day, and calendar day were also included in the model and ICUID serves as a cluster variable

Table S 2
Cox proportional hazards regression with ICU mortality as endpoint.The main variables of interest, workload based on median TISS-28-Score over up to 3 days and caseload based on median bed occupancy up to 3 days were modelled as time-dependent covariables.SAPS3, sex, year of discharge, hospital level, type of ICU, time of day, and calendar day were also included in the model and ICUID serves as a cluster variable.

Table S 3
Cox proportional hazards regression with ICU mortality as endpoint.The main variables of interest, workload based on median TISS-28-Score of up to 3 days and caseload based on median bed occupancy of up to 3 days were modelled as time-dependent covariables.SAPS3, sex, year of discharge, time of day, and calendar day were also included in the model.ICUID serves as a covariate, but is not depicted.

Table S 4
Cox proportional hazards regression with ICU mortality as endpoint.The main variables of interest, workload based on the TISS-28-Score and caseload based on bed occupancy were modelled as time-dependent covariables.SAPS3, sex, year of discharge, hospital level, type of ICU, time of day, and calendar day were also included in the model and ICUID serves as a cluster variable.

Table S 5
Cox proportional hazards regression with ICU mortality as endpoint.The main variables of interest, workload based on the TISS-28-Score and caseload based on bed occupancy were modelled as time-dependent covariables.SAPS3, sex, year of discharge, hospital level, type of ICU, time of day, and calendar day were also included in the model and ICUID serves as a cluster variable.

Calendar day (Non-working day vs. working day)
Sensitivity Analysis: Interaction Terms for Years with Caseload and Workload

Table S 6
Cox proportional hazards regression with ICU mortality as endpoint.The main variables of interest, workload based on the TISS-28-Score and caseload based on bed occupancy were modelled as time-dependent covariables.Interaction terms between years 2013-2019 and 2020-2022 with caseload and workload were also included.SAPS3, sex, year of discharge, type of ICU, time of day, and calendar day were also included in the model and ICUID serves as a cluster variable

Table S 7
Cox proportional hazards regression with ICU mortality as endpoint.The main variables of interest, workload based on the TISS-28-Score and caseload based on bed occupancy were modelled as time-dependent covariables.SAPS3, sex, year of discharge, hospital level, time of day, and calendar day were also included in the model and ICUID serves as a cluster variable.

Table S 8
Cox proportional hazards regression with ICU mortality as endpoint.The main variables of interest, workload based on the TISS-28-Score and caseload based on bed occupancy were modelled as time-dependent covariables.SAPS3, sex, year of discharge, hospital level, time of day, and calendar day were also included in the model and ICUID serves as a cluster variable.

Table S 9
Cox proportional hazards regression with ICU mortality as endpoint.The main variables of interest, workload based on the TISS-28-Score and caseload based on bed occupancy were modelled as time-dependent covariables.Interaction terms between ICU Types with caseload and workload were also included.SAPS3, sex, year of discharge, type of ICU, time of day, and calendar day were also included in the model and ICUID serves as a cluster variable

Table S 10
Cox proportional hazards regression with ICU mortality as endpoint.The main variables of interest, workload based on the TISS-28-Score and caseload based on bed occupancy were modelled as time-dependent covariables.SAPS3, sex, year of discharge, type of ICU, time of day, and calendar day were also included in the model and ICUID serves as a cluster variable.

Table S 11
Cox proportional hazards regression with ICU mortality as endpoint.The main variables of interest, workload based on the TISS-28-Score and caseload based on bed occupancy were modelled as time-dependent covariables.SAPS3, sex, year of discharge, type of ICU, time of day, and calendar day were also included in the model and ICUID serves as a cluster variable.

Table S 12
Cox proportional hazards regression with ICU mortality as endpoint.The main variables of interest, workload based on the TISS-28-Score and caseload based on bed occupancy were modelled as time-dependent covariables.SAPS3, sex, year of discharge, type of ICU, time of day, and calendar day were also included in the model and ICUID serves as a cluster variable.

Table S 13
Cox proportional hazards regression with ICU mortality as endpoint.The main variables of interest, workload based on the TISS-28-Score and caseload based on bed occupancy were modelled as time-dependent covariables.SAPS3, sex, year of discharge, type of ICU, time of day, and calendar day were also included in the model and ICUID serves as a cluster variable.

0001 Current ICU workload (TISS-28) [points] (Reference: 21 to 30)
Sensitivity Analysis: Interaction Terms for Hospital Levels with Caseload and Workload Table S 14 Cox proportional hazards regression with ICU mortality as endpoint.The main variables of interest, workload based on the TISS-28-Score and caseload based on bed occupancy were modelled as time-dependent covariables.Interaction terms between Hospital Levels with caseload and workload were also included.SAPS3, sex, year of discharge, type of ICU, time of day, and calendar day were also included in the model and ICUID serves as a cluster variable